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Partial differential equations, and their chaotic solutions, are pervasive in the modelling of complex systems in engineering, science, and beyond.
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Özalp, E., Margazoglou, G., Magri, L.: Reconstruction, forecasting, and stability of chaotic dynamics from partial data. Chaos: An Interdisciplinary Journal of Nonlinear Science 33
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Linot, A.J., Burby, J.W., Tang, Q., Balaprakash, P., Graham, M.D., Maulik, R.: Stabilized neural ordinary differential equations for long-time forecasting of dynamical systems. Journal of Computational Physics 474
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2024
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Magri, L., Novoa, A., Özalp, E.: Prediction of chaotic dynamics from data: An introduction. In: Machine Learning for Fluid Dynamics. von Karman Institute for Fluid Dynamics, (2024)
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Özalp, E., Magri, L.: Inferring stability properties of chaotic systems on autoencoders’ latent spaces. In: NeurIPS 2024, Machine Learning and the Physical Sciences Workshop (2024)
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